Energy sensitive photon counting detector-based, x-ray computed tomography (PCD-CT) has been one of the hottest research topics lately, as it is expected to provide various clinical benefits such as enhanced tissue contrast, decreased image noise, decreased radiation dose to patient, quantitative mono-energetic CT images, and more accurate material decomposition. Tissue types such as bones, fat, muscle, and iodine-enhanced blood can then be identified, allowing software applications to, e.g., separate blood vessels from bones, quantify the fat mass, etc.
The typical approach to process PCD data consists of the following two steps. First, by applying material decomposition, density images of basis functions (discussed later), w, are reconstructed from spectral projections, i.e., counts in multi-energy bins. Second, images of linear attenuation coefficients and tissue types are estimated from w.
This sequential method decouples the two steps and makes it difficult to use a priori information on tissue types to accurately estimate linear attenuation coefficients and tissue types from photon counts. For example, tissue types may be able to effectively regularize linear attenuation coefficients than a simple edge-preserving prior. The values of neighboring pixels of linear attenuation coefficients (and w) are expected to vary smoothly and continuously if they belong to the same tissue types, while they may be discontinuous at organ boundaries. The typical values of the chemical composition and mass density of various human tissue types or organs are provided by the National Institute of Standards and Technology, from which w and linear attenuation coefficients can be calculated.
Accordingly, there is a need in the art for a method to jointly estimate images of the energy-dependent linear attenuation coefficients and tissue types from PCD data.